Computational Analysis and Deep Learning for Medical Care

18 Chapter 18Figure 18.1 Deep Neural Network (DNN).Figure 18.2 The evolution of machine learning techniques (year-wise).

1 Chapter 1 Table 1.1 Various parameters of the layers of LeNet. Table 1.2 Every column indicates which feature map in S2 are combined by the uni... Table 1.3 AlexNet layer details. Table 1.4 Various parameters of ZFNet. Table 1.5 Various parameters of VGG-16. Table 1.6 Various parameters of GoogleNet. Table 1.7 Various parameters of ResNet. Table 1.8 Comparison of ResNet-50 and ResNext-50 (32 × 4d). Table 1.9 Comparison of ResNet-50 and ResNext-50 and SE-ResNeXt-50 (32 × 4d). Table 1.10 Comparison of DenseNet. Table 1.11 Various parameters of MobileNets. Table 1.12 State-of-art of spine segmentation approaches.

2 Chapter 2 Table 2.1 History of search engines. Table 2.2 Three types of user refinement of queries. Table 2.3 Different approaches for the query suggestion techniques.

3 Chapter 3 Table 3.1 Types of liver lesions. Table 3.2 Dataset count. Table 3.3 Hyperparameter settings for training. Table 3.4 Confusion matrix for AlexNet. Table 3.5 Confusion matrix for GoogLeNet. Table 3.6 Confusion matrix for ResNet-18. Table 3.7 Confusion matrix for ResNet-50. Table 3.8 Comparison of classification accuracies.

4 Chapter 4Table 4.1 Retrieval performance of metric learning for VGG19.Table 4.2 Performance of retrieval techniques of the trained VGG19 among fine-tu...Table 4.3 PR values of various models—a comparison for CT image retrieval.Table 4.4 Recall vs. precision for proposed content-based image retrieval.Table 4.5 Loss function of proposed deep regression networks for training datase...Table 4.6 Loss function of proposed deep regression networks for validation data...Table 4.7 Land mark details (identification rates vs. distance error) for the pr...Table 4.8 Accuracy value of the proposed system.Table 4.9 Accuracy of the retrieval methods compared with the metric learning–ba...

5 Chapter 6Table 6.1 Definition of the abbreviations.

6 Chapter 7Table 7.1 Performance of proposed models on English dataset.Table 7.2 Performance of proposed model on Bangla dataset.Table 7.3 Performance of proposed model on Math Symbol dataset.

7 Chapter 8Table 8.1 ABCD factor for TDS value.Table 8.2 Classify mole according to TDS value.

8 Chapter 9Table 9.1 The confusion matrix for different classifier.Table 9.2 Performance analysis of different classifiers: Random Forest, SVM, Naï...

9 Chapter 10Table 10.1 Result analysis.

10 Chapter 11Table 11.1 Comparison of different techniques and tumor.

11 Chapter 13Table 13.1 Cognitive functions related with routine activities.Table 13.2 Situation and design features.Table 13.3 Accuracy of prediction.

12 Chapter 14Table 14.1 Accuracy comparison and mean of algorithms with baseline records.Table 14.2 Accuracy comparison and mean of algorithms with current records.

13 Chapter 15Table 15.1 Variances of Convolutional Neural Network (CNN).Table 15.2 Various issues challenges faced by researchers for using deep learnin...

14 Chapter 17Table 17.1 Comparative analysis: classification accuracy for 10 datasets—analysi...

15 Chapter 18Table 18.1 Comparison among data mining, machine learning, and deep learning.

1 Cover

2 Table of Contents

3 Title Page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

1 v

2 ii

3 iii

4 iv

5 xix

6 xx

7 1

8 2

9 3

10 4

11 5

12 6

13 7

14 8

15 9

16 10

17 11

18 12

19 13

20 14

21 15

22 16

23 17

24 18

25 19

26 20

27 21

28 22

29 23

30 24

31 25

32 26

33 27

34 28

35 29

36 30

37 31

38 32

39 33

40 34

41 35

42 36

43 37

44 38

45 39

46 40

47 41

48 42

49 43

50 44

51 45

52 46

53 47

54 48

55 49

56 50

57 51

58 53

59 54

60 55

61 56

62 57

63 58

64 59

65 60

66 61

67 62

68 63

69 64

70 65

71 66

72 67

73 68

74 69

75 70

76 71

77 72

78 73

79 74

80 75

81 76

82 77

83 79

84 80

85 81

86 82

87 83

88 84

89 85

90 86

91 87

92 88

93 89

94 90

95 91

96 92

97 93

98 94

99 95

100 96

101 97

102 98

103 99

104 100

105 101

106 102

107 103

108 104

109 105

110 106

111 107

112 109

113 110

114 111

115 112

116 113

117 114

118 115

119 116

120 117

121 118

122 119

123 120

124 121

125 122

126 123

127 124

128 125

129 126

130 127

131 128

132 129

133 131

134 132

135 133

136 134

137 135

138 136

139 137

140 138

141 139

142 140

143 141

144 142

145 143

146 144

147 145

148 146

149 147

150 148

151 149

152 150

153 151

154 152

155 153

156 154

157 155

158 156

159 157

160 158

161 159

162 160

163 161

164 162

165 163

166 164

167 165

168 166

169 167

170 168

171 169

172 170

173 171

174 172

175 173

176 174

177 175

178 176

179 177

180 178

181 179

182 180

183 181

184 182

185 183

186 184

187 185

188 186

189 187

190 188

191 189

192 190

193 191

194 192

195 193

196 194

197 195

198 196

199 197

200 198

201 199

202 200

203 201

204 202

205 203

206 204

207 205

208 206

209 207

210 208

211 209

212 210

213 211

214 212

215 213

216 214

217 215

218 217

219 218

220 219

221 220

222 221

223 222

224 223

225 224

226 225

227 226

228 227

229 228

230 229

231 230

232 231

233 232

234 233

235 234

236 235

237 236

238 237

239 239

240 240

241 241

242 242

243 243

244 244

245 245

246 246

247 247

248 248

249 249

250 250

251 251

252 252

253 253

254 254

255 255

256 257

257 258

258 259

259 260

260 261

261 262

262 263

263 264

264 265

265 266

266 267

267 268

268 269

269 270

270 271

271 272

272 273

273 274

274 275

275 276

276 277

277 278

278 279

279 280

280 281

281 282

282 283

283 284

284 285

285 286

286 287

287 288

288 289

289 290

290 291

291 292

292 293

293 294

294 295

295 296

296 297

297 298

298 299

299 300

300 301

301 302

302 303

303 304

304 305

305 306

306 307

307 308

308 309

309 310

310 311

311 312

312 313

313 314

314 315

315 316

316 317

317 318

318 319

319 320

320 321

321 322

322 323

323 324

324 325

325 326

326 327

327 329

328 330

329 331

330 332

331 333